Adaptive methods for classification in arbitrarily imbalanced and drifting data streams

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5669 LNAI, Issue , 2010, Pages 53-75

  Lichtenwalter, Ryan N ^a   Chawla, Nitesh V ^a  

^a UNIVERSITY OF NOTRE DAME   (United States)

Author keywords

concept drift; Hellinger distance; imbalanced data; Sequential learning; skew; stream mining

Indexed keywords

CONCEPT DRIFTS; HELLINGER DISTANCE; IMBALANCED DATA; SEQUENTIAL LEARNING; SKEW; STREAM MINING;

HYDRAULICS; TECHNICAL PRESENTATIONS;

DATA MINING;

EID: 77957058214     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-14640-4_5     Document Type: Conference Paper

References (31)

1. Becker, H., Arias, M.: Real-time ranking with concept drift using expert advice. In: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 86-94. ACM Press, New York (2007)
2. Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: KDD 2001: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 97-106. ACM, New York (2001)
3. Kolter, J.Z., Maloof, M.A.: Dynamic weighted majority: An ensemble method for drifting concepts. Journal of Machine Learning Research 8, 2755-2790 (2007)
4. Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Machine Learning 23, 69-101 (1996)
5. Gao, J., Fan, W., Han, J., Yu, P.S.: A general framework for mining concept-drifting data streams with skewed distributions. In: SDM 2007: Proceedings of the SIAM International Conference on Data Mining (2007)
6. Wang, H., Fan, W., Yu, P.S., Han, J.: Mining concept-drifting data streams using ensemble classifiers. In: KDD 2003: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 226-235. ACM, New York (2003)
7. Han, H., Wang, W.Y., Mao, B.H.: Borderline-smote: A new over-sampling method in imbalanced data sets learning. Advances in Intelligent Computing, 878-887 (2005)
8. Cieslak, D.A., Chawla, N.V.: Detecting fractures in classifier performance. In: ICDM 2007: Seventh IEEE International Conference on Data Mining, pp. 123-132 (2007)
9. Cieslak, D.A., Chawla, N.V.: Learning decision trees for unbalanced data. In: European Conference on Machine Learning. Springer, Heidelberg (2008)
10. Asuncion, A., Newman, D.: Uci machine learning repository (2007)
11. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
12. Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: ICML 2006: Proceedings of the 23rd international conference on Machine learning, pp. 233-240. ACM, New York (2006)
13. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research 7, 1-30 (2006)
14. Street, N.W., Kim, Y.: A streaming ensemble algorithm (sea) for large-scale classification. In: KDD 2001: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 377-382. ACM, New York (2001)
15. Haghighi, P.D., Gaber, M.M., Krishnaswamy, S., Zaslavsky, A., Seng, L.: An architecture for context-aware adaptive data stream mining. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701. Springer, Heidelberg (2007)
16. Blum, A.: Empirical support for winnow and weighted-majority algorithms: Results on a calendar scheduling domain. Machine Learning 26, 5-23 (1997)
17. Forman, G.: Tackling concept drift by temporal inductive transfer. In: SIGIR 2006: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 252-259. ACM, New York (2006)
18. Harries, M., Horn, K.: Detecting concept drift in financial time series prediction using symbolic machine learning. In: Eighth Australian Joint Conference on Artificial Intelligence, pp. 91-98. World Scientific Publishing, Singapore (1995)
19. Widmer, G.: Tracking context changes through meta-learning. Machine Learning 27, 259-286 (1997)
20. Fan, W., Huang, Y.a., Wang, H., Yu, P.S.: Active mining of data streams. In: Proceedings of the Fourth SIAM International Conference on Data Mining, Society for Industrial Mathematics, pp. 457-461 (2004)
21. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, P.W.: Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16, 341-378 (2002)
22. Ertekin, S., Huang, J., Bottou, L., Giles, L.: Learning on the border: Active learning in imbalanced data classification. In: CIKM 2007: Proceedings of the sixteenth ACM Conference on information and knowledge management, pp. 127-136. ACM, New York (2007)
23. Kelly, M.G., Hand, D.J., Adams, N.M.: The impact of changing populations on classifier performance. In: KDD 1999: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 367-371. ACM, New York (1999)
24. Kuncheva, L.I.: Classifier ensembles for detecting concept change in streaming data: Overview and perspectives. In: Proceedings of the 2nd Workshop SUEMA 2008 (ECAI 2008), pp. 5-10 (2008)
25. Schlimmer, J.C., Granger, R.H.: Incremental learning from noisy data. Machine Learning 1, 317-354 (1986)
26. Bifet, A., Gavaldá, R.: Learning from time-changing data with adaptive windowing. In: SIAM International Conference on Data Mining, SDM 2007 (2006)
27. Klinkenberg, R.: Using labeled and unlabeled data to learn drifting concepts. In: Workshop notes of the IJCAI 2001 Workshop on Learning from Temporal and Spatial Data, pp. 16-24 (2001)
28. Phua, C., Miles, K.S., Lee, V., Gayler, R.: Adaptive spike detection for resilient data stream mining. In: Proceedings of the sixth Australasian conference on Data mining and analytics (AusDM 2007), pp. 181-188. Australian Computer Society, Inc., Darlinghurst (2007)
29. Markou, M., Singh, S.: Novelty detection: A review - Part 1: Statistical approaches. Signal Processing 83, 2481-2497 (2003)
30. Korn, F., Muthukrishnan, S., Wu, Y.: Modeling skew in data streams. In: SIG-MOD 2006: Proceedings of the 2006 ACM SIGMOD international conference on Management of data, pp. 181-192. ACM, New York (2006)
31. Nishida, K., Yamauchi, K., Omori, T.: Ace: Adaptive classifiers-ensemble system for concept-drifting environments. Multiple Classifier Systems, 176-185 (2005)